






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       List[str]				):
			for param, grad_param in zip(model_a.parameters()   ,  model_b.parameters()				):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad   ,  grad_param.grad				) is False
									), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad   ,  grad_param.grad				) is True
									), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Optional[Any]=True				):
			model.train()
			__a						:  str									=     model(lowerCamelCase_				)
			__a						:  List[str]									=     F.mse_loss(lowerCamelCase_   ,  target.to(output.device				)				)
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(lowerCamelCase_				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       str=False				):
			set_seed(4_2				)
			__a						:  Any									=     RegressionModel()
			__a						:  List[str]									=     deepcopy(lowerCamelCase_				)
			__a						:  Any									=     RegressionDataset(length=8_0				)
			__a						:  Tuple									=     DataLoader(lowerCamelCase_   ,  batch_size=1_6				)
			model.to(accelerator.device				)
			if sched:
						__a						:  Tuple									=     AdamW(params=model.parameters()   ,  lr=1e-3				)
						__a						:  Tuple									=     AdamW(params=ddp_model.parameters()   ,  lr=1e-3				)
						__a						:  Optional[int]									=     LambdaLR(lowerCamelCase_   ,  lr_lambda=lambda lowerCamelCase_				: epoch**0.65				)
						__a						:  List[Any]									=     LambdaLR(lowerCamelCase_   ,  lr_lambda=lambda lowerCamelCase_				: epoch**0.65				)
			# Make a copy of `model`
			if sched:
						__a						,					__a						,					__a						,					__a						:  List[str]									=     accelerator.prepare(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
			else:
						__a						,					__a						:  Optional[Any]									=     accelerator.prepare(lowerCamelCase_   ,  lowerCamelCase_				)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]				):
			# Test when on a single CPU or GPU that the context manager does nothing
			__a						,					__a						,					__a						:  Any									=     get_training_setup(lowerCamelCase_				)
			# Use a single batch
			__a						,					__a						:  int									=     next(iter(lowerCamelCase_				)				).values()
			for iteration in range(3				):
						# Gather the distributed inputs and targs for the base model
						__a						,					__a						:  Dict									=     accelerator.gather((ddp_input, ddp_target)				)
						__a						,					__a						:  List[str]									=     input.to(accelerator.device				), target.to(accelerator.device				)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowerCamelCase_				):
												step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						else:
									# Sync grads
									step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						for param, ddp_param in zip(model.parameters()   ,  ddp_model.parameters()				):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad   ,  ddp_param.grad				), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration				)
						__a						:  Union[str, Any]									=     ddp_input[torch.randperm(len(lowerCamelCase_				)				)]

def        UpperCAmelCase__		(						lowerCamelCase_       :       Union[str, Any]				):
			# Test on distributed setup that context manager behaves properly
			__a						,					__a						,					__a						:  Any									=     get_training_setup(lowerCamelCase_				)
			# Use a single batch
			__a						,					__a						:  Optional[int]									=     next(iter(lowerCamelCase_				)				).values()
			for iteration in range(3				):
						# Gather the distributed inputs and targs for the base model
						__a						,					__a						:  List[str]									=     accelerator.gather((ddp_input, ddp_target)				)
						__a						,					__a						:  List[str]									=     input.to(accelerator.device				), target.to(accelerator.device				)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowerCamelCase_				):
												step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						else:
									# Sync grads
									step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()   ,  ddp_model.parameters()				):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad   ,  ddp_param.grad				) is False
												), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad   ,  ddp_param.grad				) is True
												), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration				)
						__a						:  int									=     ddp_input[torch.randperm(len(lowerCamelCase_				)				)]

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]=False   ,  lowerCamelCase_       :       Union[str, Any]=False				):
			__a						:  Optional[int]									=     Accelerator(
			    split_batches=lowerCamelCase_   ,  dispatch_batches=lowerCamelCase_   ,  gradient_accumulation_steps=2				)
			# Test that context manager behaves properly
			__a						,					__a						,					__a						:  Optional[int]									=     get_training_setup(lowerCamelCase_				)
			for iteration, batch in enumerate(lowerCamelCase_				):
						__a						,					__a						:  Dict									=     batch.values()
						# Gather the distributed inputs and targs for the base model
						__a						,					__a						:  Optional[Any]									=     accelerator.gather((ddp_input, ddp_target)				)
						__a						,					__a						:  int									=     input.to(accelerator.device				), target.to(accelerator.device				)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(lowerCamelCase_				):
									step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()   ,  ddp_model.parameters()				):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase_				) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad   ,  ddp_param.grad				) is True
												), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad   ,  ddp_param.grad				) is False
												), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration				)
						__a						:  Union[str, Any]									=     ddp_input[torch.randperm(len(lowerCamelCase_				)				)]
			GradientState._reset_state()

def        UpperCAmelCase__		(						lowerCamelCase_       :       str=False   ,  lowerCamelCase_       :       Optional[int]=False				):
			__a						:  Any									=     Accelerator(
			    split_batches=lowerCamelCase_   ,  dispatch_batches=lowerCamelCase_   ,  gradient_accumulation_steps=2				)
			# Test that context manager behaves properly
			__a						,					__a						,					__a						,					__a						,					__a						,					__a						,					__a						:  int									=     get_training_setup(lowerCamelCase_   ,  lowerCamelCase_				)
			for iteration, batch in enumerate(lowerCamelCase_				):
						__a						,					__a						:  Optional[int]									=     batch.values()
						# Gather the distributed inputs and targs for the base model
						__a						,					__a						:  Optional[Any]									=     accelerator.gather((ddp_input, ddp_target)				)
						__a						,					__a						:  int									=     input.to(accelerator.device				), target.to(accelerator.device				)
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase_				)):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes				):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(lowerCamelCase_				):
									step_model(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
						__a						:  Tuple									=     (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase_				))
						if accelerator.num_processes > 1:
									check_model_parameters(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration				)
			GradientState._reset_state()

def        UpperCAmelCase__		(						):
			__a						:  List[Any]									=     Accelerator()

			__a						:  List[Any]									=     RegressionDataset(length=8_0				)
			__a						:  str									=     DataLoader(lowerCamelCase_   ,  batch_size=1_6				)
			__a						:  List[str]									=     RegressionDataset(length=9_6				)
			__a						:  Optional[int]									=     DataLoader(lowerCamelCase_   ,  batch_size=1_6				)
			__a						,					__a						:  Any									=     accelerator.prepare(lowerCamelCase_   ,  lowerCamelCase_				)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(lowerCamelCase_				):
						assert id(accelerator.gradient_state.active_dataloader				) == id(lowerCamelCase_				)
						if iteration < len(lowerCamelCase_				) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(lowerCamelCase_				):
															assert id(accelerator.gradient_state.active_dataloader				) == id(lowerCamelCase_				)
															if batch_num < len(lowerCamelCase_				) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None

def        UpperCAmelCase__		(						):
			__a						:  int									=     Accelerator()
			__a						:  Optional[Any]									=     accelerator.state
			if state.local_process_index == 0:
						print('**Test `accumulate` gradient accumulation with dataloader break**'				)
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print('**Test NOOP `no_sync` context manager**'				)
						test_noop_sync(lowerCamelCase_				)
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print('**Test Distributed `no_sync` context manager**'				)
						test_distributed_sync(lowerCamelCase_				)
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    '**Test `accumulate` gradient accumulation, '   ,  f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''   ,  )
												test_gradient_accumulation(lowerCamelCase_   ,  lowerCamelCase_				)

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version('<'   ,  '2.0'				) or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '   ,  '`split_batches=False`, `dispatch_batches=False`**'   ,  )
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '   ,  f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''   ,  )
															test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase_   ,  lowerCamelCase_				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple				):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

